An attention‐supervised full‐resolution residual network for the segmentation of breast ultrasound images

Background Approximately 500 000 patients present with critical limb ischemia (CLI) each year in the U.S., requiring revascularization to avoid amputation. While peripheral arteries can be revascularized via minimally invasive procedures, 25% of cases with chronic total occlusions are unsuccessful due to inability to route the guidewire beyond the proximal occlusion. Improvements to guidewire navigation would lead to limb salvage in a greater number of patients. Purpose Integrating ultrasound imaging into the guidewire could enable direct visualization of routes for guidewire advancement. In order to navigate a robotically‐steerable guidewire with integrated imaging beyond a chronic occlusion proximal to the symptomatic lesion for revascularization, acquired ultrasound images must be segmented to visualize the path for guidewire advancement. Methods The first approach for automated segmentation of viable paths through occlusions in peripheral arteries is demonstrated in simulations and experimentally‐acquired data with a forward‐viewing, robotically‐steered guidewire imaging system. B‐mode ultrasound images formed via synthetic aperture focusing (SAF) were segmented using a supervised approach (U‐net architecture). A total of 2500 simulated images were used to train the classifier to distinguish the vessel wall and occlusion from viable paths for guidewire advancement. First, the size of the synthetic aperture resulting in the highest classification performance was determined in simulations (90 test images) and compared with traditional classifiers (global thresholding, local adaptive thresholding, and hierarchical classification). Next, classification performance as a function of the diameter of the remaining lumen (0.5 to 1.5 mm) in the partially‐occluded artery was tested using both simulated (60 test images at each of 7 diameters) and experimental data sets. Experimental test data sets were acquired in four 3D‐printed phantoms from human anatomy and six ex vivo porcine arteries. Accuracy of classifying the path through the artery was evaluated using microcomputed tomography of phantoms and ex vivo arteries as a ground truth for comparison. Results An aperture size of 3.8 mm resulted in the best‐performing classification based on sensitivity and Jaccard index, with a significant increase in Jaccard index (p < 0.05) as aperture diameter increased. In comparing the performance of the supervised classifier and traditional classification strategies with simulated test data, sensitivity and F1 score for U‐net were 0.95 ± 0.02 and 0.96 ± 0.01, respectively, compared to 0.83 ± 0.03 and 0.41 ± 0.13 for the best‐performing conventional approach, hierarchical classification. In simulated test images, sensitivity (p < 0.05) and Jaccard index both increased with increasing artery diameter (p < 0.05). Classification of images acquired in artery phantoms with remaining lumen diameters ≥ 0.75 mm resulted in accuracies > 90%, while mean accuracy decreased to 82% when artery diameter decreased to 0.5 mm. For testing i...

show abstract

“…If the intersection of the two groups represent a large percent of the total area of the two groups, the metric produces a value close to one. 85…”

Section: Quantification Of Classifier Performancementioning

confidence: 99%

Supervised segmentation for guiding peripheral revascularization with forward‐viewing, robotically steered ultrasound guidewire

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The first dataset (Dataset A) is an open BUS dataset from the Sun Yat‐sen University Cancer Center 36 . The dataset was reorganized, 37 and 974 samples were used in the experiment and obtained using an iU22 xMATRIX scanner (Philips, USA) or a LOGIQ E9 scanner (GE, USA). The input of each sample was a BUS image, and the output label of each sample was from the patient's pathological results.…”

Section: Experiments Setupmentioning

confidence: 99%

A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images

Wang

et al. 2022

Medical Physics

Self Cite

View full text Add to dashboard Cite

Purpose Breast cancer is the most commonly occurring cancer worldwide. The ultrasound reflectivity imaging technique can be used to obtain breast ultrasound (BUS) images, which can be used to classify benign and malignant tumors. However, the classification is subjective and dependent on the experience and skill of operators and doctors. The automatic classification method can assist doctors and improve the objectivity, but current convolution neural network (CNN) is not good at learning global features and vision transformer (ViT) is not good at extraction local features. In this study, we proposed a visual geometry group attention ViT (VGGA‐ViT) network to overcome their disadvantages. Methods In the proposed method, we used a CNN module to extract the local features and employed a ViT module to learn the global relationship among different regions and enhance the relevant local features. The CNN module was named the VGGA module. It was composed of a VGG backbone, a feature extraction fully connected layer, and a squeeze‐and‐excitation block. Both the VGG backbone and the ViT module were pretrained on the ImageNet dataset and retrained using BUS samples in this study. Two BUS datasets were employed for validation. Results Cross‐validation was conducted on two BUS datasets. For the Dataset A, the proposed VGGA‐ViT network achieved high accuracy (88.71±$\ \pm \ $1.55%), recall (90.73±$\ \pm \ $1.57%), specificity (85.58±$\ \pm \ $3.35%), precision (90.77±$\ \pm \ $1.98%), F1 score (90.73±$\ \pm \ $1.24%), and Matthews correlation coefficient (MCC) (76.34±7$\ \pm \ 7$3.29%), which were better than those of all compared previous networks in this study. The Dataset B was used as a separate test set, the test results showed that the VGGA‐ViT had highest accuracy (81.72±$\ \pm \ $2.99%), recall (64.45±$\ \pm \ $2.96%), specificity (90.28±$\ \pm \ $3.51%), precision (77.08±$\ \pm \ $7.21%), F1 score (70.11±$\ \pm \ $4.25%), and MCC (57.64±$\ \pm \ $6.88%). Conclusions In this study, we proposed the VGGA‐ViT for the BUS classification, which was good at learning both local and global features. The proposed network achieved higher accuracy than the compared previous methods.

show abstract

“…19 They evaluated their model against 10 other FCN's using BUSIS, BUSI, and UDIAT. Qu et al 20 discussed the use of a full-resolution residual network, integrated with Global Attention Upsample and deep supervision. The model was tested on two datasets: one from Sun Yat-sen University Cancer Center and the other being UDIAT.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…They evaluated their model against 10 other FCN's using BUSIS, BUSI, and UDIAT. Qu et al 20 . discussed the use of a full‐resolution residual network, integrated with Global Attention Upsample and deep supervision.…”

Section: Introduction and Related Workmentioning

confidence: 99%

BUS‐Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets

et al. 2023

View full text Add to dashboard Cite

BUS-Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field of BUS. Method: Four publicly available datasets were compiled creating an overall set of 1154 BUS images, from five different scanner types. Full dataset details have been provided, which include clinical labels and detailed annotations. Furthermore, nine state-of -the-art deep learning architectures were selected to form the initial benchmark segmentation result, tested using five-fold cross-validation and MANOVA/ANOVA with Tukey statistical significance test with a threshold of 0.01. Additional evaluation of these architectures was conducted, exploring possible training bias, and lesion size and type effects. Results: Of the nine state-of -the-art benchmarked architectures, Mask R-CNN obtained the highest overall results, with the following mean metric scores: Dice score of 0.851, intersection over union of 0.786 and pixel accuracy of 0.975. MANOVA/ANOVA and Tukey test results showed Mask R-CNN to be statistically significant better compared to all other benchmarked models with a p-value > 0.01. Moreover, Mask R-CNN achieved the highest mean Dice score of 0.839 on an additional 16 image dataset, that contained multiple lesions per image. Further analysis on regions of interest was conducted, assessing Hamming distance, depth-to-width ratio (DWR), circularity, and elongation, which showed that the Mask R-CNN's segmentations maintained the most morphological features with correlation coefficients of 0.888, 0.532, 0.876 for DWR, circularity, and elongation, respectively. Based on the correlation coefficients, statistical test indicated that Mask R-CNN was only significantly different to Sk-U-Net. Conclusions: BUS-Set is a fully reproducible benchmark for BUS lesion segmentation obtained through the use of public datasets and GitHub. Of the state-of -the-art convolution neural network (CNN)-based architectures, Mask R-CNN achieved the highest performance overall, further analysis indicated that a training bias may have occurred due to the lesion size variation in the dataset. All dataset and architecture details are available at GitHub: https://github.com/ corcor27/BUS-Set, which allows for a fully reproducible benchmark.

show abstract

An attention‐supervised full‐resolution residual network for the segmentation of breast ultrasound images

Cited by 27 publications

References 35 publications

Supervised segmentation for guiding peripheral revascularization with forward‐viewing, robotically steered ultrasound guidewire

Supervised segmentation for guiding peripheral revascularization with forward‐viewing, robotically steered ultrasound guidewire

A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images

BUS‐Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets

Contact Info

Product

Resources

About